first commit

models/Chronos.py

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+import torch
+from torch import nn
+from layers.Transformer_EncDec import Encoder, EncoderLayer
+from layers.SelfAttention_Family import FullAttention, AttentionLayer
+from layers.Embed import PatchEmbedding
+from chronos import BaseChronosPipeline
+
+
+class Model(nn.Module):
+    def __init__(self, configs):
+        """
+        patch_len: int, patch len for patch_embedding
+        stride: int, stride for patch_embedding
+        """
+        super().__init__()
+        self.model = BaseChronosPipeline.from_pretrained(
+            "amazon/chronos-bolt-base",
+            device_map="cuda",  # use "cpu" for CPU inference and "mps" for Apple Silicon
+            torch_dtype=torch.bfloat16,
+        )
+        self.task_name = configs.task_name
+        self.seq_len = configs.seq_len
+        self.pred_len = configs.pred_len
+
+    def forecast(self, x_enc, x_mark_enc, x_dec, x_mark_dec):
+        outputs = []
+        for i in range(x_enc.shape[-1]):
+            output = self.model.predict(x_enc[...,i], prediction_length=self.pred_len)
+            output = output.mean(dim=1)
+            outputs.append(output)
+        dec_out = torch.stack(outputs, dim=-1)
+
+        return dec_out
+
+    def forward(self, x_enc, x_mark_enc, x_dec, x_mark_dec, mask=None):
+        if self.task_name == 'zero_shot_forecast':
+            dec_out = self.forecast(x_enc, x_mark_enc, x_dec, x_mark_dec)
+            return dec_out
+        return None